A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

U

UNARY_FUNCTIONS - Static variable in class weka.filters.unsupervised.attribute.AddExpression

UNCONNECTED - Static variable in class weka.classifiers.functions.neural.NeuralConnection

This unit is not connected to any others.

USAGE - Static variable in class weka.core.converters.ArffFileMerger

UnassignedClassException - exception weka.core.UnassignedClassException.

Exception that is raised when trying to use some data that has no class assigned to it, but a class is needed to perform the operation.

UnassignedClassException() - Constructor for class weka.core.UnassignedClassException

Creates a new UnassignedClassException with no message.

UnassignedClassException(String) - Constructor for class weka.core.UnassignedClassException

Creates a new UnassignedClassException.

UnassignedDatasetException - exception weka.core.UnassignedDatasetException.

Exception that is raised when trying to use something that has no reference to a dataset, when one is required.

UnassignedDatasetException() - Constructor for class weka.core.UnassignedDatasetException

Creates a new UnassignedDatasetException with no message.

UnassignedDatasetException(String) - Constructor for class weka.core.UnassignedDatasetException

Creates a new UnassignedDatasetException.

UnsupervisedAttributeEvaluator - class weka.attributeSelection.UnsupervisedAttributeEvaluator.

Abstract unsupervised attribute evaluator.

UnsupervisedAttributeEvaluator() - Constructor for class weka.attributeSelection.UnsupervisedAttributeEvaluator

UnsupervisedFilter - interface weka.filters.UnsupervisedFilter.

Interface for filters that do not need a class attribute.

UnsupervisedSubsetEvaluator - class weka.attributeSelection.UnsupervisedSubsetEvaluator.

Abstract unsupervised attribute subset evaluator.

UnsupervisedSubsetEvaluator() - Constructor for class weka.attributeSelection.UnsupervisedSubsetEvaluator

UnsupportedAttributeTypeException - exception weka.core.UnsupportedAttributeTypeException.

Exception that is raised by an object that is unable to process some of the attribute types it has been passed.

UnsupportedAttributeTypeException() - Constructor for class weka.core.UnsupportedAttributeTypeException

Creates a new UnsupportedAttributeTypeException with no message.

UnsupportedAttributeTypeException(String) - Constructor for class weka.core.UnsupportedAttributeTypeException

Creates a new UnsupportedAttributeTypeException.

UnsupportedClassTypeException - exception weka.core.UnsupportedClassTypeException.

Exception that is raised by an object that is unable to process the class type of the data it has been passed.

UnsupportedClassTypeException() - Constructor for class weka.core.UnsupportedClassTypeException

Creates a new UnsupportedClassTypeException with no message.

UnsupportedClassTypeException(String) - Constructor for class weka.core.UnsupportedClassTypeException

Creates a new UnsupportedClassTypeException.

UpdateableClassifier - interface weka.classifiers.UpdateableClassifier.

Interface to incremental classification models that can learn using one instance at a time.

UserClassifier - class weka.classifiers.trees.UserClassifier.

Class for generating an user defined decision tree.

UserClassifier() - Constructor for class weka.classifiers.trees.UserClassifier

Constructor

UserClassifier.TreeClass - class weka.classifiers.trees.UserClassifier.TreeClass.

Inner class used to represent the actual decision tree structure and data.

UserClassifier.TreeClass(FastVector, int, int, int, double, Instances, UserClassifier.TreeClass) - Constructor for class weka.classifiers.trees.UserClassifier.TreeClass

Constructs a TreeClass node with all the important information.

UserRequestAcceptor - interface weka.gui.beans.UserRequestAcceptor.

Interface to something that can accept requests from a user to perform some action

Utils - class weka.core.Utils.

Class implementing some simple utility methods.

Utils() - Constructor for class weka.core.Utils

uBCenter(int, int, int[]) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine

uConnectivity(int, int) - Method in class weka.gui.graphvisualizer.HierarchicalBCEngine

uminus() - Method in class weka.classifiers.functions.pace.Matrix

Unary minus

unclassified() - Method in class weka.classifiers.Evaluation

Gets the number of instances not classified (that is, for which no prediction was made by the classifier).

uncover - Variable in class weka.classifiers.rules.ConjunctiveRule.Antd

The parameters related to the data not covered by the previous antecedents when the class is nominal

uncoverSum - Variable in class weka.classifiers.rules.ConjunctiveRule.Antd

The parameter related to the meanSquaredError of the data not covered by the previous antecedents when the class is numeric

uncoverWtSq - Variable in class weka.classifiers.rules.ConjunctiveRule.Antd

The parameter related to the meanSquaredError of the data not covered by the previous antecedents when the class is numeric

uncoverWtVl - Variable in class weka.classifiers.rules.ConjunctiveRule.Antd

The parameter related to the meanSquaredError of the data not covered by the previous antecedents when the class is numeric

undefinedDistribution - Static variable in class weka.classifiers.functions.pace.Maths

Distribution type: undefined

undo() - Method in class weka.gui.explorer.PreprocessPanel

Reverts to the last backed up version of the dataset.

unifDist - Variable in class weka.gui.visualize.MatrixPanel

For selecting uniform class distribution in the subsample

unique() - Method in class weka.classifiers.functions.pace.DiscreteFunction

Makes each individual point value unique

uniqueCount - Variable in class weka.core.AttributeStats

The number of values that only appear once

unpivoting(IntVector, int) - Method in class weka.classifiers.functions.pace.DoubleVector

Returns a vector from the pivoting indices.

unprune() - Method in class weka.classifiers.trees.lmt.LMTNode

Method to "unprune" a logistic model tree.

unpruned - Variable in class weka.classifiers.rules.part.MakeDecList

Generated unpruned list?

unprunedTipText() - Method in class weka.classifiers.rules.PART

Returns the tip text for this property

unprunedTipText() - Method in class weka.classifiers.trees.J48

Returns the tip text for this property

unsorted() - Method in class weka.classifiers.functions.pace.DoubleVector

Returns true if vector not sorted

untangle() - Method in class weka.gui.treevisualizer.PlaceNode2

This will untangle the nodes in the tree so that they fall on the correct side of each other.

untangle2() - Method in class weka.gui.treevisualizer.PlaceNode2

This untangles the nodes so that they will will fall on the correct side of the other nodes along their row.

upDate(Instances) - Method in class weka.associations.tertius.LiteralSet

Update the number of counter-instances of this set in the dataset.

upDate(Instances) - Method in class weka.associations.tertius.Rule

Update the number of counter-instances of this rule in the dataset.

upDateCounter(Instance) - Method in class weka.associations.ItemSet

Updates counter of item set with respect to given transaction.

upDateCounters(FastVector, Instances) - Static method in class weka.associations.ItemSet

Updates counters for a set of item sets and a set of instances.

update(double) - Method in class weka.classifiers.functions.pace.FlexibleDecimalFormat

update() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee

update(Instance) - Method in class weka.classifiers.rules.NNge

Performs the update of the classifier

update() - Method in class weka.gui.boundaryvisualizer.BoundaryPanel

updateChart(double[]) - Method in class weka.gui.beans.StripChart

Update the plot

updateChildPropertySheet() - Method in class weka.gui.GenericObjectEditor.GOEPanel

Updates the child property sheet, and creates if needed

updateCholeskyFactor(Matrix, double[], double[], double, boolean[]) - Method in class weka.core.Optimization

One rank update of the Cholesky factorization of B matrix in BFGS updates, i.e.

updateClassifier(Instance) - Method in interface weka.classifiers.UpdateableClassifier

Updates a classifier using the given instance.

updateClassifier(Instance) - Method in class weka.classifiers.bayes.BayesNet

Updates the classifier with the given instance.

updateClassifier(Instance) - Method in class weka.classifiers.bayes.NaiveBayes

Updates the classifier with the given instance.

updateClassifier(Instance) - Method in class weka.classifiers.functions.Winnow

Updates the classifier with a new learning example

updateClassifier(Instance) - Method in class weka.classifiers.lazy.IB1

Updates the classifier.

updateClassifier(Instance) - Method in class weka.classifiers.lazy.IBk

Adds the supplied instance to the training set

updateClassifier(Instance) - Method in class weka.classifiers.lazy.KStar

Adds the supplied instance to the training set

updateClassifier(Instance) - Method in class weka.classifiers.lazy.LWL

Adds the supplied instance to the training set

updateClassifier(Instance) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

Updates the classifier.

updateClassifier(Instance) - Method in class weka.classifiers.misc.HyperPipes

Updates the classifier.

updateClassifier(Instance) - Method in class weka.classifiers.rules.NNge

Updates the classifier using the given instance.

updateDecisionList(Random, Instance) - Method in class weka.datagenerators.RDG1

Generates a new rule for the decision list.

updateDisplay() - Method in class weka.classifiers.functions.MultilayerPerceptron

Call this function to update the control panel for the gui.

updateEditor() - Method in class weka.gui.PropertyText

Attempts to update the editor value from the textfield.

updateEditorType(Object) - Method in class weka.gui.GenericArrayEditor

Updates the type of object being edited, so attempts to find an appropriate propertyeditor.

updateFS(Instance, Classifier[], double[]) - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost.Committee

updateLegends() - Method in class weka.gui.visualize.LegendPanel

Redraw the panel with the legend entries

updateMI(Instance) - Method in class weka.classifiers.rules.NNge

Updates the data for computing the mutual information MUST be called AFTER adding inst in m_Train

updateMargins(double[], int, double) - Method in class weka.classifiers.Evaluation

Update the cumulative record of classification margins

updateMinDistance(double[], boolean[], Instances, Instance) - Method in class weka.clusterers.FarthestFirst

updateMinMax(Instance) - Method in class weka.attributeSelection.ReliefFAttributeEval

Updates the minimum and maximum values for all the attributes based on a new instance.

updateMinMax(Instance) - Method in class weka.classifiers.lazy.IB1

Updates the minimum and maximum values for all the attributes based on a new instance.

updateMinMax(Instance) - Method in class weka.classifiers.lazy.IBk

Updates the minimum and maximum values for all the attributes based on a new instance.

updateMinMax(Instance) - Method in class weka.classifiers.lazy.LWL

Updates the minimum and maximum values for all the attributes based on a new instance.

updateMinMax(Instance) - Method in class weka.classifiers.rules.NNge

Updates the minimum, maximum, sum, sumSquare values for all the attributes

updateMinMax(Instance) - Method in class weka.clusterers.EM

Updates the minimum and maximum values for all the attributes based on a new instance.

updateMinMax(Instance) - Method in class weka.clusterers.FarthestFirst

Updates the minimum and maximum values for all the attributes based on a new instance.

updateMinMax(Instance) - Method in class weka.clusterers.SimpleKMeans

Updates the minimum and maximum values for all the attributes based on a new instance.

updateMinMax(Instance) - Method in class weka.gui.boundaryvisualizer.KDDataGenerator

Updates the minimum and maximum values for all the attributes based on a new instance.

updateMonitor() - Method in class weka.gui.WekaTaskMonitor

Updates the number of running tasks an the status of the bird image

updateNetworkWeights(double, double) - Method in class weka.classifiers.functions.MultilayerPerceptron

This will cause the weight values to be updated based on the learning rate, momentum and the errors that have been calculated for each node.

updateNumericScores(double[], double[], double) - Method in class weka.classifiers.Evaluation

Update the numeric accuracy measures.

updateObjectNames() - Method in class weka.gui.GenericObjectEditor

Updates the list of selectable object names, adding any new names to the list.

updateOptions() - Method in class weka.experiment.ClassifierSplitEvaluator

Updates the options that the current classifier is using.

updateOptions() - Method in class weka.experiment.RegressionSplitEvaluator

Updates the options that the current classifier is using.

updateOutput() - Method in class weka.gui.streams.InstanceViewer

updatePredicted() - Method in class weka.classifiers.evaluation.NominalPrediction

Determines the predicted class (doesn't detect multiple classifications).

updatePriors(Instance) - Method in class weka.classifiers.Evaluation

Updates the class prior probabilities (when incrementally training)

updatePturb() - Method in class weka.gui.visualize.Plot2D

Updates the perturbed values for the plots when the jitter value is changed

updateRadioLinks() - Method in class weka.gui.experiment.DistributeExperimentPanel

Updates the remote experiment when a radio button is clicked

updateRadioLinks() - Method in class weka.gui.experiment.SetupPanel

Updates the primary loop iteration control of the experiment

updateRadioLinks() - Method in class weka.gui.explorer.AttributeSelectionPanel

Updates the enabled status of the input fields and labels.

updateRadioLinks() - Method in class weka.gui.explorer.ClassifierPanel

Updates the enabled status of the input fields and labels.

updateRadioLinks() - Method in class weka.gui.explorer.ClustererPanel

Updates the enabled status of the input fields and labels.

updateResult(String) - Method in class weka.gui.ResultHistoryPanel

Tells any component currently displaying the named result that the contents of the result text in the StringBuffer have been updated.

updateResultsTableName(ResultProducer) - Method in class weka.experiment.DatabaseResultListener

Determines the table name that results will be inserted into.

updateStats(Instance, boolean) - Method in class weka.clusterers.Cobweb.CNode

Update attribute stats using the supplied instance.

updateStatsForClassifier(double[], Instance) - Method in class weka.classifiers.Evaluation

Updates all the statistics about a classifiers performance for the current test instance.

updateStatsForPredictor(double, Instance) - Method in class weka.classifiers.Evaluation

Updates all the statistics about a predictors performance for the current test instance.

updateUs() - Method in class weka.gui.PropertyText

Attempts to update the textfield value from the editor.

updateWeights(NeuralNode, double, double) - Method in class weka.classifiers.functions.neural.LinearUnit

This function will calculate what the change in weights should be and also update them.

updateWeights(double, double) - Method in class weka.classifiers.functions.neural.NeuralConnection

Call this function to update the weight values at this unit.

updateWeights(NeuralNode, double, double) - Method in interface weka.classifiers.functions.neural.NeuralMethod

This function will calculate what the change in weights should be and also update them.

updateWeights(double, double) - Method in class weka.classifiers.functions.neural.NeuralNode

Call this function to update the weight values at this unit.

updateWeights(NeuralNode, double, double) - Method in class weka.classifiers.functions.neural.SigmoidUnit

This function will calculate what the change in weights should be and also update them.

updateWeights(Instances, Instances, double) - Method in class weka.classifiers.trees.ADTree

Updates the weights of instances that are influenced by a new prediction value.

updateWeightsDiscreteClass(int) - Method in class weka.attributeSelection.ReliefFAttributeEval

update attribute weights given an instance when the class is discrete

updateWeightsNumericClass(int) - Method in class weka.attributeSelection.ReliefFAttributeEval

update attribute weights given an instance when the class is numeric

update_m_Attributes() - Method in class weka.classifiers.lazy.KStar

Updates the m_attributes of the class.

updateableClassifier() - Method in class weka.classifiers.CheckClassifier

Checks whether the scheme can build models incrementally.

updatingEquality(boolean, boolean, boolean) - Method in class weka.classifiers.CheckClassifier

Checks whether an updateable scheme produces the same model when trained incrementally as when batch trained.

upperBoundMinSupportTipText() - Method in class weka.associations.Apriori

Returns the tip text for this property

upperNumericBoundIsOpen() - Method in class weka.core.Attribute

Returns whether the upper numeric bound of the attribute is open.

upperSizeTipText() - Method in class weka.experiment.LearningRateResultProducer

Returns the tip text for this property

usage() - Static method in class weka.core.converters.HierarchicalCostMatrix

Describes the proper usage of this class.

useADTreeTipText() - Method in class weka.classifiers.bayes.BayesNet

useBetterEncodingTipText() - Method in class weka.filters.supervised.attribute.Discretize

Returns the tip text for this property

useCrossValidationTipText() - Method in class weka.classifiers.functions.SimpleLogistic

Returns the tip text for this property

useDefaultVisual() - Method in class weka.gui.beans.AbstractDataSink

Use the default images for a data source

useDefaultVisual() - Method in class weka.gui.beans.AbstractDataSource

Use the default images for a data source

useDefaultVisual() - Method in class weka.gui.beans.AbstractEvaluator

Use the default images for an evaluator

useDefaultVisual() - Method in class weka.gui.beans.AbstractTestSetProducer

Use the default visual for this bean

useDefaultVisual() - Method in class weka.gui.beans.AbstractTrainAndTestSetProducer

Use the default visual for this bean

useDefaultVisual() - Method in class weka.gui.beans.AbstractTrainingSetProducer

Use the default visual for this bean

useDefaultVisual() - Method in class weka.gui.beans.AttributeSummarizer

Use the default appearance for this bean

useDefaultVisual() - Method in class weka.gui.beans.ClassAssigner

useDefaultVisual() - Method in class weka.gui.beans.Classifier

Use the default visual appearance for this bean

useDefaultVisual() - Method in class weka.gui.beans.DataVisualizer

Use the default appearance for this bean

useDefaultVisual() - Method in class weka.gui.beans.Filter

Use the default visual appearance

useDefaultVisual() - Method in class weka.gui.beans.GraphViewer

Use the default visual appearance

useDefaultVisual() - Method in class weka.gui.beans.PredictionAppender

Use the default images for a data source

useDefaultVisual() - Method in class weka.gui.beans.StripChart

Use the default visual appearance for this bean

useDefaultVisual() - Method in class weka.gui.beans.TextViewer

Use the default visual appearance for this bean

useDefaultVisual() - Method in interface weka.gui.beans.Visible

Use the default visual representation

useEqualFrequencyTipText() - Method in class weka.filters.unsupervised.attribute.Discretize

Returns the tip text for this property

useEqualFrequencyTipText() - Method in class weka.filters.unsupervised.attribute.PKIDiscretize

Returns the tip text for this property

useFilter(Instances, Filter) - Static method in class weka.filters.Filter

Filters an entire set of instances through a filter and returns the new set.

useIBkTipText() - Method in class weka.classifiers.rules.DecisionTable

Returns the tip text for this property

useKernelEstimatorTipText() - Method in class weka.classifiers.bayes.NaiveBayes

Returns the tip text for this property

useKononenkoTipText() - Method in class weka.filters.supervised.attribute.Discretize

Returns the tip text for this property

useLaplaceTipText() - Method in class weka.classifiers.trees.J48

Returns the tip text for this property

useMissingTipText() - Method in class weka.filters.unsupervised.attribute.AddNoise

Returns the tip text for this property

usePruningTipText() - Method in class weka.classifiers.rules.JRip

Returns the tip text for this property

useRBFTipText() - Method in class weka.classifiers.functions.SMO

Returns the tip text for this property

useRBFTipText() - Method in class weka.classifiers.functions.SMOreg

Returns the tip text for this property

useResamplingTipText() - Method in class weka.classifiers.meta.AdaBoostM1

Returns the tip text for this property

useResamplingTipText() - Method in class weka.classifiers.meta.LogitBoost

Returns the tip text for this property

useResamplingTipText() - Method in class weka.classifiers.meta.RacedIncrementalLogitBoost

useStoplistTipText() - Method in class weka.filters.unsupervised.attribute.StringToWordVector

Returns the tip text for this property.

useSupervisedDiscretizationTipText() - Method in class weka.classifiers.bayes.NaiveBayes

Returns the tip text for this property

useTrainingTipText() - Method in class weka.attributeSelection.ClassifierSubsetEval

Returns the tip text for this property

userCommand(TreeDisplayEvent) - Method in class weka.classifiers.trees.UserClassifier

Receives user choices from the tree view, and then deals with these choices.

userCommand(TreeDisplayEvent) - Method in interface weka.gui.treevisualizer.TreeDisplayListener

Gets called when the user selects something, in the tree display.

userDataEvent(VisualizePanelEvent) - Method in class weka.classifiers.trees.UserClassifier

This receives shapes from the data view.

userDataEvent(VisualizePanelEvent) - Method in interface weka.gui.visualize.VisualizePanelListener

This method receives an object containing the shapes, instances inside and outside these shapes and the attributes these shapes were created in.

userWantsToConvert() - Method in class weka.gui.experiment.SimpleSetupPanel

Gets te users consent for converting the experiment to a simpler form.